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  <div class="section" id="base">
<h1>Base<a class="headerlink" href="#base" title="Permalink to this headline"></a></h1>
<div class="section" id="layertype">
<h2>LayerType<a class="headerlink" href="#layertype" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">LayerType</code></dt>
<dd><p>Layer type enumerations.</p>
<dl class="staticmethod">
<dt>
<em class="property">static </em><code class="descname">is_layer_type</code><span class="sig-paren">(</span><em>type_name</em><span class="sig-paren">)</span></dt>
<dd><p>If type_name is a layer type.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>type_name</strong> (<em>basestring</em>) &#8211; layer type name. Because layer type enumerations are
strings.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">True if is a layer_type</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">bool</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="layeroutput">
<h2>LayerOutput<a class="headerlink" href="#layeroutput" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">LayerOutput</code><span class="sig-paren">(</span><em>name</em>, <em>layer_type</em>, <em>parents=None</em>, <em>activation=None</em>, <em>num_filters=None</em>, <em>img_norm_type=None</em>, <em>size=None</em>, <em>outputs=None</em>, <em>reverse=None</em><span class="sig-paren">)</span></dt>
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<dd><p>LayerOutput is output for layer function. It is used internally by several
reasons.</p>
<ul>
<li><p class="first">Check layer connection make sense.</p>
<blockquote>
<div><ul class="simple">
<li>FC(Softmax) =&gt; Cost(MSE Error) is not good for example.</li>
</ul>
</div></blockquote>
</li>
<li><p class="first">Tracking layer connection.</p>
</li>
<li><p class="first">Pass to layer methods as input.</p>
</li>
</ul>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer output name.</li>
<li><strong>layer_type</strong> (<em>basestring</em>) &#8211; Current Layer Type. One of LayerType enumeration.</li>
<li><strong>activation</strong> (<em>BaseActivation.</em>) &#8211; Layer Activation.</li>
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<li><strong>parents</strong> (<em>list|tuple|collection.Sequence</em>) &#8211; Layer&#8217;s parents.</li>
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</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="data-layer">
<h1>Data layer<a class="headerlink" href="#data-layer" title="Permalink to this headline"></a></h1>
<div class="section" id="id1">
<h2>data_layer<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">data_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Define DataLayer For NeuralNetwork.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;input&quot;</span><span class="p">,</span>
                  <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Name of this data layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; Size of this data layer.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="fully-connected-layers">
<h1>Fully Connected Layers<a class="headerlink" href="#fully-connected-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="fc-layer">
<h2>fc_layer<a class="headerlink" href="#fc-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">fc_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>Helper for declare fully connected layer.</p>
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<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fc</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
              <span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
              <span class="n">act</span><span class="o">=</span><span class="n">LinearActivation</span><span class="p">(),</span>
              <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<p>which is equal to:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span> <span class="k">as</span> <span class="n">fc</span><span class="p">:</span>
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    <span class="n">fc</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>input</strong> (<em>LayerOutput|list|tuple</em>) &#8211; The input layer. Could be a list/tuple of input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The layer dimension.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type. Default is tanh.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; The Parameter Attribute|list.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
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something not type of ParameterAttribute. None will get a
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default Bias.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra Layer config.</li>
</ul>
</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
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</tr>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
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</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="selective-fc-layer">
<h2>selective_fc_layer<a class="headerlink" href="#selective-fc-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">selective_fc_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Selectived fully connected layer. Different from fc_layer, the output
of this layer maybe sparse. It requires an additional input to indicate
several selected columns for output. If the selected columns is not
specified, selective_fc_layer acts exactly like fc_layer.</p>
<p>The simple usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sel_fc</span> <span class="o">=</span> <span class="n">selective_fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">())</span>
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</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>input</strong> (<em>LayerOutput|list|tuple</em>) &#8211; The input layer.</li>
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<li><strong>select</strong> (<em>LayerOutput</em>) &#8211; The select layer. The output of select layer should be a
sparse binary matrix, and treat as the mask of selective fc.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; The layer dimension.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type. Default is tanh.</li>
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<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; The Parameter Attribute.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra Layer config.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="conv-layers">
<h1>Conv Layers<a class="headerlink" href="#conv-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="conv-operator">
<h2>conv_operator<a class="headerlink" href="#conv-operator" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
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<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">conv_operator</code><span class="sig-paren">(</span><em>img</em>, <em>filter</em>, <em>filter_size</em>, <em>num_filters</em>, <em>num_channel=None</em>, <em>stride=1</em>, <em>padding=0</em>, <em>filter_size_y=None</em>, <em>stride_y=None</em>, <em>padding_y=None</em><span class="sig-paren">)</span></dt>
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<dd><p>Different from img_conv_layer, conv_op is an Operator, which can be used
in mixed_layer. And conv_op takes two inputs to perform convolution.
The first input is the image and the second is filter kernel. It only
support GPU mode.</p>
<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">op</span> <span class="o">=</span> <span class="n">conv_operator</span><span class="p">(</span><span class="n">img</span><span class="o">=</span><span class="n">input1</span><span class="p">,</span>
                   <span class="nb">filter</span><span class="o">=</span><span class="n">input2</span><span class="p">,</span>
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                   <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
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                   <span class="n">num_filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
                   <span class="n">num_channels</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>img</strong> (<em>LayerOutput</em>) &#8211; input image</li>
<li><strong>filter</strong> (<em>LayerOutput</em>) &#8211; input filter</li>
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<li><strong>filter_size</strong> (<em>int</em>) &#8211; The x dimension of a filter kernel.</li>
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<li><strong>filter_size_y</strong> (<em>int</em>) &#8211; The y dimension of a filter kernel. Since
PaddlePaddle now supports rectangular filters,
the filter&#8217;s shape can be (filter_size, filter_size_y).</li>
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<li><strong>num_filters</strong> (<em>int</em>) &#8211; channel of output data.</li>
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<li><strong>num_channel</strong> (<em>int</em>) &#8211; channel of input data.</li>
<li><strong>stride</strong> (<em>int</em>) &#8211; The x dimension of the stride.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The y dimension of the stride.</li>
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<li><strong>padding</strong> (<em>int</em>) &#8211; The x dimension of padding.</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The y dimension of padding.</li>
</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A ConvOperator Object.</p>
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</td>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">ConvOperator</p>
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</td>
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</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="conv-shift-layer">
<h2>conv_shift_layer<a class="headerlink" href="#conv-shift-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">conv_shift_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><dl class="docutils">
<dt>This layer performs cyclic convolution for two input. For example:</dt>
<dd><ul class="first last simple">
<li>a[in]: contains M elements.</li>
<li>b[in]: contains N elements (N should be odd).</li>
<li>c[out]: contains M elements.</li>
</ul>
</dd>
</dl>
<div class="math">
\[c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}\]</div>
<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
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<li>a&#8217;s index is computed modulo M. When it is negative, then get item from
the right side (which is the end of array) to the left.</li>
<li>b&#8217;s index is computed modulo N. When it is negative, then get item from
the right size (which is the end of array) to the left.</li>
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</ul>
</dd>
</dl>
<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">conv_shift</span> <span class="o">=</span> <span class="n">conv_shift_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">])</span>
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</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name</li>
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<li><strong>a</strong> (<em>LayerOutput</em>) &#8211; Input layer a.</li>
<li><strong>b</strong> (<em>LayerOutput</em>) &#8211; input layer b</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="img-conv-layer">
<h2>img_conv_layer<a class="headerlink" href="#img-conv-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">img_conv_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Convolution layer for image. Paddle only support square input currently and
thus input image&#8217;s width equals height.</p>
<p>The details of convolution layer, please refer UFLDL&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/">convolution</a> .</p>
<p>The num_channel means input image&#8217;s channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer&#8217;s
num_filters * num_group.</p>
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<p>There are several group of filter in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
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32*4 = 128 filters to process inputs. The channels will be split into 4
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pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Layer Input.</li>
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<li><strong>filter_size</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of a filter kernel. Or input a tuple for
two image dimension.</li>
<li><strong>filter_size_y</strong> (<em>int|None</em>) &#8211; The y dimension of a filter kernel. Since PaddlePaddle
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currently supports rectangular filters, the filter&#8217;s
shape will be (filter_size, filter_size_y).</li>
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<li><strong>num_filters</strong> &#8211; Each filter group&#8217;s number of filter</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation type. Default is tanh</li>
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<li><strong>groups</strong> (<em>int</em>) &#8211; Group size of filters.</li>
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<li><strong>stride</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of the stride. Or input a tuple for two image
dimension.</li>
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<li><strong>stride_y</strong> (<em>int</em>) &#8211; The y dimension of the stride.</li>
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<li><strong>padding</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of the padding. Or input a tuple for two
image dimension</li>
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<li><strong>padding_y</strong> (<em>int</em>) &#8211; The y dimension of the padding.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Convolution bias attribute. None means default bias.
False means no bias.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; number of input channels. If None will be set
automatically from previous output.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Convolution param attribute. None means default attribute</li>
<li><strong>shared_biases</strong> (<em>bool</em>) &#8211; Is biases will be shared between filters or not.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Layer Extra Attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="context-projection">
<h2>context_projection<a class="headerlink" href="#context-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">context_projection</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Context Projection.</p>
<p>It just simply reorganizes input sequence, combines &#8220;context_len&#8221; sequence
to one context from context_start. &#8220;context_start&#8221; will be set to
-(context_len - 1) / 2 by default. If context position out of sequence
length, padding will be filled as zero if padding_attr = False, otherwise
it is trainable.</p>
<p>For example, origin sequence is [A B C D E F G], context len is 3, then
after context projection and not set padding_attr, sequence will
be [ 0AB ABC BCD CDE DEF EFG FG0 ].</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Sequence.</li>
<li><strong>context_len</strong> (<em>int</em>) &#8211; context length.</li>
<li><strong>context_start</strong> (<em>int</em>) &#8211; context start position. Default is
-(context_len - 1)/2</li>
<li><strong>padding_attr</strong> (<em>bool|ParameterAttribute</em>) &#8211; Padding Parameter Attribute. If false, it means padding
always be zero. Otherwise Padding is learnable, and
parameter attribute is set by this parameter.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Projection</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Projection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="image-pooling-layer">
<h1>Image Pooling Layer<a class="headerlink" href="#image-pooling-layer" title="Permalink to this headline"></a></h1>
<div class="section" id="img-pool-layer">
<h2>img_pool_layer<a class="headerlink" href="#img-pool-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">img_pool_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Image pooling Layer.</p>
<p>The details of pooling layer, please refer ufldl&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/Pooling/">pooling</a> .</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>padding</strong> (<em>int</em>) &#8211; pooling padding width.</li>
<li><strong>padding_y</strong> (<em>int|None</em>) &#8211; pooling padding height. It&#8217;s equal to padding by default.</li>
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<li><strong>name</strong> (<em>basestring.</em>) &#8211; name of pooling layer</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input</li>
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<li><strong>pool_size</strong> (<em>int</em>) &#8211; pooling window width</li>
<li><strong>pool_size_y</strong> (<em>int|None</em>) &#8211; pooling window height. It&#8217;s eaqual to pool_size by default.</li>
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<li><strong>num_channels</strong> (<em>int</em>) &#8211; number of input channel.</li>
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<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; pooling type. MaxPooling or AveragePooling. Default is
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MaxPooling.</li>
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<li><strong>stride</strong> (<em>int</em>) &#8211; stride width of pooling.</li>
<li><strong>stride_y</strong> (<em>int|None</em>) &#8211; stride height of pooling. It is equal to stride by default.</li>
<li><strong>start</strong> (<em>int|None</em>) &#8211; start position of pooling operation. Note it is deprecated now.</li>
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<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer attribute.</li>
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<li><strong>img_width</strong> (<em>int|None</em>) &#8211; the width of input feature map. If it is None, the input feature
map should be square.</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="norm-layer">
<h1>Norm Layer<a class="headerlink" href="#norm-layer" title="Permalink to this headline"></a></h1>
<div class="section" id="img-cmrnorm-layer">
<h2>img_cmrnorm_layer<a class="headerlink" href="#img-cmrnorm-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">img_cmrnorm_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>Response normalization across feature maps.
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The details please refer to
<a class="reference external" href="http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf">Alex&#8217;s paper</a>.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>name</strong> (<em>None|basestring</em>) &#8211; layer name.</li>
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; Normalize in number of <span class="math">\(size\)</span> feature maps.</li>
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<li><strong>scale</strong> (<em>float</em>) &#8211; The hyper-parameter.</li>
<li><strong>power</strong> (<em>float</em>) &#8211; The hyper-parameter.</li>
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<li><strong>num_channels</strong> &#8211; input layer&#8217;s filers number or channels. If
num_channels is None, it will be set automatically.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="batch-norm-layer">
<h2>batch_norm_layer<a class="headerlink" href="#batch-norm-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">batch_norm_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Batch Normalization Layer. The notation of this layer as follow.</p>
<p><span class="math">\(x\)</span> is the input features over a mini-batch.</p>
<div class="math">
\[\begin{split}\mu_{\beta} &amp;\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &amp;//\
\ mini-batch\ mean \\
\sigma_{\beta}^{2} &amp;\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \
\mu_{\beta})^2 \qquad &amp;//\ mini-batch\ variance \\
\hat{x_i} &amp;\gets \frac{x_i - \mu_\beta} {\sqrt{\
\sigma_{\beta}^{2} + \epsilon}} \qquad &amp;//\ normalize \\
y_i &amp;\gets \gamma \hat{x_i} + \beta \qquad &amp;//\ scale\ and\ shift\end{split}\]</div>
<p>The details of batch normalization please refer to this
<a class="reference external" href="http://arxiv.org/abs/1502.03167">paper</a>.</p>
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<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; batch normalization input. Better be linear activation.
Because there is an activation inside batch_normalization.</li>
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<li><strong>batch_norm_type</strong> (<em>None|string, None or &quot;batch_norm&quot; or &quot;cudnn_batch_norm&quot;</em>) &#8211; We have batch_norm and cudnn_batch_norm. batch_norm
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supports both CPU and GPU. cudnn_batch_norm requires
cuDNN version greater or equal to v4 (&gt;=v4). But
cudnn_batch_norm is faster and needs less memory
than batch_norm. By default (None), we will
automaticly select cudnn_batch_norm for GPU and
batch_norm for CPU. Otherwise, select batch norm
type based on the specified type. If you use cudnn_batch_norm,
we suggested you use latest version, such as v5.1.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type. Better be relu. Because batch
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normalization will normalize input near zero.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; num of image channels or previous layer&#8217;s number of
filters. None will automatically get from layer&#8217;s
input.</li>
<li><strong>bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; <span class="math">\(\beta\)</span>, better be zero when initialize. So the
initial_std=0, initial_mean=1 is best practice.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; <span class="math">\(\gamma\)</span>, better be one when initialize. So the
initial_std=0, initial_mean=1 is best practice.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
<li><strong>use_global_stats</strong> (<em>bool|None.</em>) &#8211; whether use moving mean/variance statistics
during testing peroid. If None or True,
it will use moving mean/variance statistics during
testing. If False, it will use the mean
and variance of current batch of test data for
testing.</li>
<li><strong>moving_average_fraction</strong> (<em>float.</em>) &#8211; Factor used in the moving average
computation, referred to as facotr,
<span class="math">\(runningMean = newMean*(1-factor)
+ runningMean*factor\)</span></li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="sum-to-one-norm-layer">
<h2>sum_to_one_norm_layer<a class="headerlink" href="#sum-to-one-norm-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sum_to_one_norm_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for sum-to-one normalization,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[out[i] = \frac {in[i]} {\sum_{k=1}^N in[k]}\]</div>
<p>where <span class="math">\(in\)</span> is a (batchSize x dataDim) input vector,
and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sum_to_one_norm</span> <span class="o">=</span> <span class="n">sum_to_one_norm_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="recurrent-layers">
<h1>Recurrent Layers<a class="headerlink" href="#recurrent-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="recurrent-layer">
<h2>recurrent_layer<a class="headerlink" href="#recurrent-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">recurrent_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>Simple recurrent unit layer. It is just a fully connect layer through both
time and neural network.</p>
<p>For each sequence [start, end] it performs the following computation:</p>
<div class="math">
\[\begin{split}out_{i} = act(in_{i})     \      \      \text{for} \ i = start \\
out_{i} = act(in_{i} + out_{i-1} * W) \ \ \text{for} \ start &lt; i &lt;= end\end{split}\]</div>
<p>If reversed is true, the order is reversed:</p>
<div class="math">
\[\begin{split}out_{i} = act(in_{i})           \    \   \text{for} \ i = end  \\
out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start &lt;= i &lt; end\end{split}\]</div>
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; activation.</li>
<li><strong>bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; bias attribute.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; parameter attribute.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the layer</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Layer Attribute.</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="lstmemory">
<h2>lstmemory<a class="headerlink" href="#lstmemory" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">lstmemory</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Long Short-term Memory Cell.</p>
<p>The memory cell was implemented as follow equations.</p>
<div class="math">
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\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
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<p>NOTE: In PaddlePaddle&#8217;s implementation, the multiplications
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<span class="math">\(W_{xi}x_{t}\)</span> , <span class="math">\(W_{xf}x_{t}\)</span>,
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<span class="math">\(W_{xc}x_t\)</span>, <span class="math">\(W_{xo}x_{t}\)</span> are not done in the lstmemory layer,
so an additional mixed_layer with full_matrix_projection or a fc_layer must
be included in the configuration file to complete the input-to-hidden
mappings before lstmemory is called.</p>
<p>NOTE: This is a low level user interface. You can use network.simple_lstm
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to config a simple plain lstm layer.</p>
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<p>Please refer to <strong>Generating Sequences With Recurrent Neural Networks</strong> for
more details about LSTM.</p>
<p><a class="reference external" href="http://arxiv.org/abs/1308.0850">Link</a> goes as below.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; The lstmemory layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; is sequence process reversed or not.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; activation type, TanhActivation by default. <span class="math">\(h_t\)</span></li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; gate activation type, SigmoidActivation by default.</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; state activation type, TanhActivation by default.</li>
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<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Parameter Attribute.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra Layer attribute</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="lstm-step-layer">
<h2>lstm_step_layer<a class="headerlink" href="#lstm-step-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">lstm_step_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>LSTM Step Layer. It used in recurrent_group. The lstm equations are shown
as follow.</p>
<div class="math">
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\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
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<p>The input of lstm step is <span class="math">\(Wx_t + Wh_{t-1}\)</span>, and user should use
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<code class="code docutils literal"><span class="pre">mixed_layer</span></code> and <code class="code docutils literal"><span class="pre">full_matrix_projection</span></code> to calculate these
input vector.</p>
<p>The state of lstm step is <span class="math">\(c_{t-1}\)</span>. And lstm step layer will do</p>
<div class="math">
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\[ \begin{align}\begin{aligned}i_t = \sigma(input + W_{ci}c_{t-1} + b_i)\\...\end{aligned}\end{align} \]</div>
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<p>This layer contains two outputs. Default output is <span class="math">\(h_t\)</span>. The other
output is <span class="math">\(o_t\)</span>, which name is &#8216;state&#8217; and can use
<code class="code docutils literal"><span class="pre">get_output_layer</span></code> to extract this output.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer&#8217;s name.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; Layer&#8217;s size. NOTE: lstm layer&#8217;s size, should be equal as
<code class="code docutils literal"><span class="pre">input.size/4</span></code>, and should be equal as
<code class="code docutils literal"><span class="pre">state.size</span></code>.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer. <span class="math">\(Wx_t + Wh_{t-1}\)</span></li>
<li><strong>state</strong> (<em>LayerOutput</em>) &#8211; State Layer. <span class="math">\(c_{t-1}\)</span></li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation type. Default is tanh</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; Gate Activation Type. Default is sigmoid, and should
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be sigmoid only.</li>
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<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; State Activation Type. Default is sigmoid, and should
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be sigmoid only.</li>
<li><strong>bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Bias Attribute.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; layer&#8217;s extra attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="grumemory">
<h2>grumemory<a class="headerlink" href="#grumemory" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">grumemory</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Gate Recurrent Unit Layer.</p>
<p>The memory cell was implemented as follow equations.</p>
<p>1. update gate <span class="math">\(z\)</span>: defines how much of the previous memory to
keep around or the unit updates its activations. The update gate
is computed by:</p>
<div class="math">
\[z_t = \sigma(W_{z}x_{t} + U_{z}h_{t-1} + b_z)\]</div>
<p>2. reset gate <span class="math">\(r\)</span>: determines how to combine the new input with the
previous memory. The reset gate is computed similarly to the update gate:</p>
<div class="math">
\[r_t = \sigma(W_{r}x_{t} + U_{r}h_{t-1} + b_r)\]</div>
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<p>3. The candidate activation <span class="math">\(\tilde{h_t}\)</span> is computed similarly to
that of the traditional recurrent unit:</p>
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<div class="math">
\[{\tilde{h_t}} = tanh(W x_{t} + U (r_{t} \odot h_{t-1}) + b)\]</div>
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<p>4. The hidden activation <span class="math">\(h_t\)</span> of the GRU at time t is a linear
interpolation between the previous activation <span class="math">\(h_{t-1}\)</span> and the
candidate activation <span class="math">\(\tilde{h_t}\)</span>:</p>
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<div class="math">
\[h_t = (1 - z_t) h_{t-1} + z_t {\tilde{h_t}}\]</div>
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<p>NOTE: In PaddlePaddle&#8217;s implementation, the multiplication operations
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<span class="math">\(W_{r}x_{t}\)</span>, <span class="math">\(W_{z}x_{t}\)</span> and <span class="math">\(W x_t\)</span> are not computed in
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gate_recurrent layer. Consequently, an additional mixed_layer with
full_matrix_projection or a fc_layer must be included before grumemory
is called.</p>
<p>More details can be found by referring to <a class="reference external" href="https://arxiv.org/abs/1412.3555">Empirical Evaluation of Gated
Recurrent Neural Networks on Sequence Modeling.</a></p>
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<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">grumemory</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The gru layer name.</li>
<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; input layer.</li>
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<li><strong>reverse</strong> (<em>bool</em>) &#8211; Whether sequence process is reversed or not.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; activation type, TanhActivation by default. This activation
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affects the <span class="math">\({\tilde{h_t}}\)</span>.</li>
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<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; gate activation type, SigmoidActivation by default.
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This activation affects the <span class="math">\(z_t\)</span> and <span class="math">\(r_t\)</span>. It is the
<span class="math">\(\sigma\)</span> in the above formula.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Parameter Attribute.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra Layer attribute</li>
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<li><strong>size</strong> (<em>None</em>) &#8211; Stub parameter of size, but actually not used. If set this size
will get a warning.</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="gru-step-layer">
<h2>gru_step_layer<a class="headerlink" href="#gru-step-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">gru_step_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; </li>
<li><strong>output_mem</strong> &#8211; </li>
<li><strong>size</strong> &#8211; </li>
<li><strong>act</strong> &#8211; </li>
<li><strong>name</strong> &#8211; </li>
<li><strong>gate_act</strong> &#8211; </li>
<li><strong>bias_attr</strong> &#8211; </li>
<li><strong>layer_attr</strong> &#8211; </li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="recurrent-layer-group">
<h1>Recurrent Layer Group<a class="headerlink" href="#recurrent-layer-group" title="Permalink to this headline"></a></h1>
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<div class="section" id="recurrent-group">
<h2>recurrent_group<a class="headerlink" href="#recurrent-group" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">recurrent_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>Recurrent layer group is an extremely flexible recurrent unit in
PaddlePaddle. As long as the user defines the calculation done within a
time step, PaddlePaddle will iterate such a recurrent calculation over
sequence input. This is extremely usefull for attention based model, or
Neural Turning Machine like models.</p>
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<p>The basic usage (time steps) is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                      <span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
                      <span class="n">act</span><span class="o">=</span><span class="n">LinearActivation</span><span class="p">(),</span>
                      <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">output</span>

<span class="n">group</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                        <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
</pre></div>
</div>
<p>You can see following configs for further usages:</p>
<ul class="simple">
<li>time steps: lstmemory_group, paddle/gserver/tests/sequence_layer_group.conf,                   demo/seqToseq/seqToseq_net.py</li>
<li>sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf</li>
</ul>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>step</strong> (<em>callable</em>) &#8211; <p>recurrent one time step function.The input of this function is
input of the group. The return of this function will be
recurrent group&#8217;s return value.</p>
<p>The recurrent group scatter a sequence into time steps. And
for each time step, will invoke step function, and return
a time step result. Then gather each time step of output into
layer group&#8217;s output.</p>
</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; recurrent_group&#8217;s name.</li>
<li><strong>input</strong> (<em>LayerOutput|StaticInput|SubsequenceInput|list|tuple</em>) &#8211; <p>Input links array.</p>
<p>LayerOutput will be scattered into time steps.
SubsequenceInput will be scattered into sequence steps.
StaticInput will be imported to each time step, and doesn&#8217;t change
through time. It&#8217;s a mechanism to access layer outside step function.</p>
</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; If reverse is set true, the recurrent unit will process the
input sequence in a reverse order.</li>
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<li><strong>targetInlink</strong> (<em>LayerOutput|SubsequenceInput</em>) &#8211; <p>the input layer which share info with layer group&#8217;s output</p>
<p>Param input specifies multiple input layers. For
SubsequenceInput inputs, config should assign one input
layer that share info(the number of sentences and the number
of words in each sentence) with all layer group&#8217;s outputs.
targetInlink should be one of the layer group&#8217;s input.</p>
</li>
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</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="beam-search">
<h2>beam_search<a class="headerlink" href="#beam-search" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">beam_search</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Beam search is a heuristic search algorithm used in sequence generation.
It explores a graph by expanding the most promising nodes in a limited set
to maintain tractability.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_step</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
    <span class="n">last_time_step_output</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;rnn&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</span><span class="p">)</span>
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    <span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;rnn&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">simple_rnn</span><span class="p">:</span>
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        <span class="n">simple_rnn</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
        <span class="n">simple_rnn</span> <span class="o">+=</span> <span class="n">last_time_step_output</span>
    <span class="k">return</span> <span class="n">simple_rnn</span>

<span class="n">beam_gen</span> <span class="o">=</span> <span class="n">beam_search</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;decoder&quot;</span><span class="p">,</span>
                       <span class="n">step</span><span class="o">=</span><span class="n">rnn_step</span><span class="p">,</span>
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                       <span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">StaticInput</span><span class="p">(</span><span class="n">encoder_last</span><span class="p">)],</span>
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                       <span class="n">bos_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                       <span class="n">eos_id</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
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                       <span class="n">beam_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
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</pre></div>
</div>
<p>Please see the following demo for more details:</p>
<ul class="simple">
<li>machine translation : demo/seqToseq/translation/gen.conf                             demo/seqToseq/seqToseq_net.py</li>
</ul>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>base string</em>) &#8211; Name of the recurrent unit that generates sequences.</li>
<li><strong>step</strong> (<em>callable</em>) &#8211; <p>A callable function that defines the calculation in a time
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step, and it is applied to sequences with arbitrary length by
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sharing a same set of weights.</p>
<p>You can refer to the first parameter of recurrent_group, or
demo/seqToseq/seqToseq_net.py for more details.</p>
</li>
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<li><strong>input</strong> (<em>list</em>) &#8211; Input data for the recurrent unit</li>
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<li><strong>bos_id</strong> (<em>int</em>) &#8211; Index of the start symbol in the dictionary. The start symbol
is a special token for NLP task, which indicates the
beginning of a sequence. In the generation task, the start
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symbol is essential, since it is used to initialize the RNN
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internal state.</li>
<li><strong>eos_id</strong> (<em>int</em>) &#8211; Index of the end symbol in the dictionary. The end symbol is
a special token for NLP task, which indicates the end of a
sequence. The generation process will stop once the end
symbol is generated, or a pre-defined max iteration number
is exceeded.</li>
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<li><strong>max_length</strong> (<em>int</em>) &#8211; Max generated sequence length.</li>
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<li><strong>beam_size</strong> (<em>int</em>) &#8211; Beam search for sequence generation is an iterative search
algorithm. To maintain tractability, every iteration only
only stores a predetermined number, called the beam_size,
of the most promising next words. The greater the beam
size, the fewer candidate words are pruned.</li>
<li><strong>num_results_per_sample</strong> (<em>int</em>) &#8211; Number of the generated results per input
sequence. This number must always be less than
beam size.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The generated word index.</p>
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</td>
</tr>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
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<div class="section" id="get-output-layer">
<h2>get_output_layer<a class="headerlink" href="#get-output-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">get_output_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>Get layer&#8217;s output by name. In PaddlePaddle, a layer might return multiple
values, but returns one layer&#8217;s output. If the user wants to use another
output besides the default one, please use get_output_layer first to get
the output from input.</p>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer&#8217;s name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; get output layer&#8217;s input. And this layer should contains
multiple outputs.</li>
<li><strong>arg_name</strong> (<em>basestring</em>) &#8211; Output name from input.</li>
<li><strong>layer_attr</strong> &#8211; Layer&#8217;s extra attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="mixed-layer">
<h1>Mixed Layer<a class="headerlink" href="#mixed-layer" title="Permalink to this headline"></a></h1>
<div class="section" id="id2">
<h2>mixed_layer<a class="headerlink" href="#id2" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">mixed_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Mixed Layer. A mixed layer will add all inputs together, then activate.
Each inputs is a projection or operator.</p>
<p>There are two styles of usages.</p>
<ol class="arabic simple">
<li>When not set inputs parameter, use mixed_layer like this:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">)</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li>You can also set all inputs when invoke mixed_layer as follows:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
                <span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">),</span>
                       <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer2</span><span class="p">)])</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
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<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; mixed layer name. Can be referenced by other layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; layer size.</li>
<li><strong>input</strong> &#8211; inputs layer. It is an optional parameter. If set,
then this function will just return layer&#8217;s name.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type.</li>
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<li><strong>bias_attr</strong> (<em>ParameterAttribute or None or bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; The extra layer config. Default is None.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">MixedLayerType object can add inputs or layer name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">MixedLayerType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="embedding-layer">
<h2>embedding_layer<a class="headerlink" href="#embedding-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">embedding_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Define a embedding Layer.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Name of this embedding layer.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layer for this embedding. NOTE: must be Index Data.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The embedding dimension.</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute|None</em>) &#8211; The embedding parameter attribute. See ParameterAttribute
for details.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra layer Config. Default is None.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="dotmul-projection">
<h2>dotmul_projection<a class="headerlink" href="#dotmul-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">dotmul_projection</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>DotMulProjection with a layer as input.
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It performs element-wise multiplication with weight.</p>
<div class="math">
\[out.row[i] += in.row[i] .* weight\]</div>
<p>where <span class="math">\(.*\)</span> means element-wise multiplication.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">dotmul_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
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<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A DotMulProjection Object.</p>
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</td>
</tr>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DotMulProjection</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="dotmul-operator">
<h2>dotmul_operator<a class="headerlink" href="#dotmul-operator" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
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<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">dotmul_operator</code><span class="sig-paren">(</span><em>a=None</em>, <em>b=None</em>, <em>scale=1</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>DotMulOperator takes two inputs and performs element-wise multiplication:</p>
<div class="math">
\[out.row[i] += scale * (x.row[i] .* y.row[i])\]</div>
<p>where <span class="math">\(.*\)</span> means element-wise multiplication, and
scale is a config scalar, its default value is one.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">op</span> <span class="o">=</span> <span class="n">dotmul_operator</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>a</strong> (<em>LayerOutput</em>) &#8211; Input layer1</li>
<li><strong>b</strong> (<em>LayerOutput</em>) &#8211; Input layer2</li>
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<li><strong>scale</strong> (<em>float</em>) &#8211; config scalar, default value is one.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A DotMulOperator Object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DotMulOperator</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="full-matrix-projection">
<h2>full_matrix_projection<a class="headerlink" href="#full-matrix-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">full_matrix_projection</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Full Matrix Projection. It performs full matrix multiplication.</p>
<div class="math">
\[out.row[i] += in.row[i] * weight\]</div>
<p>There are two styles of usage.</p>
<ol class="arabic simple">
<li>When used in mixed_layer like this, you can only set the input:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li>When used as an independant object like this, you must set the size:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                              <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                              <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A FullMatrixProjection Object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">FullMatrixProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="identity-projection">
<h2>identity_projection<a class="headerlink" href="#identity-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">identity_projection</code><span class="sig-paren">(</span><em>input</em>, <em>offset=None</em><span class="sig-paren">)</span></dt>
<dd><ol class="arabic simple">
<li>IdentityProjection if offset=None. It performs:</li>
</ol>
<div class="math">
\[out.row[i] += in.row[i]\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<p>2. IdentityOffsetProjection if offset!=None. It likes IdentityProjection,
but layer size may be smaller than input size.
It select dimesions [offset, offset+layer_size) from input:</p>
<div class="math">
\[out.row[i] += in.row[i + \textrm{offset}]\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                           <span class="n">offset</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that both of two projections should not have any parameter.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer.</li>
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<li><strong>offset</strong> (<em>int</em>) &#8211; Offset, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A IdentityProjection or IdentityOffsetProjection Object</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">IdentityProjection or IdentityOffsetProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="table-projection">
<h2>table_projection<a class="headerlink" href="#table-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">table_projection</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Table Projection. It selects rows from parameter where row_id
is in input_ids.</p>
<div class="math">
\[out.row[i] += table.row[ids[i]]\]</div>
<p>where <span class="math">\(out\)</span> is output, <span class="math">\(table\)</span> is parameter, <span class="math">\(ids\)</span> is input_ids,
and <span class="math">\(i\)</span> is row_id.</p>
<p>There are two styles of usage.</p>
<ol class="arabic simple">
<li>When used in mixed_layer like this, you can only set the input:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">table_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li>When used as an independant object like this, you must set the size:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">table_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                        <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                        <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer, which must contains id fields.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A TableProjection Object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">TableProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="trans-full-matrix-projection">
<h2>trans_full_matrix_projection<a class="headerlink" href="#trans-full-matrix-projection" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">trans_full_matrix_projection</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Different from full_matrix_projection, this projection performs matrix
multiplication, using transpose of weight.</p>
<div class="math">
\[out.row[i] += in.row[i] * w^\mathrm{T}\]</div>
<p><span class="math">\(w^\mathrm{T}\)</span> means transpose of weight.
The simply usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">trans_full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                                    <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                                    <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span>
                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">,</span>
                                         <span class="n">initial_mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
                                         <span class="n">initial_std</span><span class="o">=</span><span class="mf">0.01</span><span class="p">))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A TransposedFullMatrixProjection Object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">TransposedFullMatrixProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="aggregate-layers">
<h1>Aggregate Layers<a class="headerlink" href="#aggregate-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="pooling-layer">
<h2>pooling_layer<a class="headerlink" href="#pooling-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">pooling_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Pooling layer for sequence inputs, not used for Image.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq_pool</span> <span class="o">=</span> <span class="n">pooling_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                         <span class="n">pooling_type</span><span class="o">=</span><span class="n">AvgPooling</span><span class="p">(),</span>
                         <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">EACH_SEQUENCE</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>agg_level</strong> (<em>AggregateLevel</em>) &#8211; AggregateLevel.EACH_TIMESTEP or
AggregateLevel.EACH_SEQUENCE</li>
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<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>pooling_type</strong> (<em>BasePoolingType|None</em>) &#8211; Type of pooling, MaxPooling(default), AvgPooling,
SumPooling, SquareRootNPooling.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Bias parameter attribute. False if no bias.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; The Extra Attributes for layer, such as dropout.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="last-seq">
<h2>last_seq<a class="headerlink" href="#last-seq" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">last_seq</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Get Last Timestamp Activation of a sequence.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>agg_level</strong> &#8211; Aggregated level</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="first-seq">
<h2>first_seq<a class="headerlink" href="#first-seq" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">first_seq</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Get First Timestamp Activation of a sequence.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>agg_level</strong> &#8211; aggregation level</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="concat-layer">
<h2>concat_layer<a class="headerlink" href="#concat-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">concat_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Concat all input vector into one huge vector.
Inputs can be list of LayerOutput or list of projection.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
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<li><strong>input</strong> (<em>list|tuple|collection.Sequence</em>) &#8211; input layers or projections</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation type.</li>
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<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="reshaping-layers">
<h1>Reshaping Layers<a class="headerlink" href="#reshaping-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="block-expand-layer">
<h2>block_expand_layer<a class="headerlink" href="#block-expand-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">block_expand_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><dl class="docutils">
<dt>Expand feature map to minibatch matrix.</dt>
<dd><ul class="first last simple">
<li>matrix width is: block_y * block_x * channel</li>
<li>matirx height is: outputH * outputW</li>
</ul>
</dd>
</dl>
<div class="math">
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\[ \begin{align}\begin{aligned}outputH = 1 + (2 * padding_y + imgSizeH - block_y + stride_y - 1) / stride_y\\outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x\end{aligned}\end{align} \]</div>
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<p>The expand method is the same with ExpandConvLayer, but saved the transposed
value. After expanding, output.sequenceStartPositions will store timeline.
The number of time steps are outputH * outputW and the dimension of each
time step is block_y * block_x * channel. This layer can be used after
convolution neural network, and before recurrent neural network.</p>
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<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">block_expand</span> <span class="o">=</span> <span class="n">block_expand_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span>
                                  <span class="n">channel</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
                                  <span class="n">stride_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">stride_y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">block_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">block_x</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
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<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layer.</li>
<li><strong>channel</strong> (<em>int</em>) &#8211; The channel number of input layer.</li>
<li><strong>block_x</strong> (<em>int</em>) &#8211; The width of sub block.</li>
<li><strong>block_y</strong> (<em>int</em>) &#8211; The width of sub block.</li>
<li><strong>stride_x</strong> (<em>int</em>) &#8211; The stride size in horizontal direction.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The stride size in vertical direction.</li>
<li><strong>padding_x</strong> (<em>int</em>) &#8211; The padding size in horizontal direction.</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The padding size in vertical direction.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layer, which can not specify.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="expand-layer">
<h2>expand_layer<a class="headerlink" href="#expand-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">expand_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for &#8220;Expand Dense data or (sequence data where the length of each
sequence is one) to sequence data.&#8221;</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">expand</span> <span class="o">=</span> <span class="n">expand_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span>
                      <span class="n">expand_as</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span>
                      <span class="n">expand_level</span><span class="o">=</span><span class="n">ExpandLevel</span><span class="o">.</span><span class="n">FROM_TIMESTEP</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer</li>
<li><strong>expand_as</strong> (<em>LayerOutput</em>) &#8211; Expand as this layer&#8217;s sequence info.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>expand_level</strong> (<em>ExpandLevel</em>) &#8211; whether input layer is timestep(default) or sequence.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="math-layers">
<h1>Math Layers<a class="headerlink" href="#math-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="addto-layer">
<h2>addto_layer<a class="headerlink" href="#addto-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">addto_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>AddtoLayer.</p>
<div class="math">
\[y = f(\sum_{i} x_i + b)\]</div>
<p>where <span class="math">\(y\)</span> is output, <span class="math">\(x\)</span> is input, <span class="math">\(b\)</span> is bias,
and <span class="math">\(f\)</span> is activation function.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">addto</span> <span class="o">=</span> <span class="n">addto_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span>
                    <span class="n">act</span><span class="o">=</span><span class="n">ReluActivation</span><span class="p">(),</span>
                    <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<p>This layer just simply add all input layers together, then activate the sum
inputs. Each input of this layer should be the same size, which is also the
output size of this layer.</p>
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<p>There is no weight matrix for each input, because it just a simple add
operation. If you want a complicated operation before add, please use
mixed_layer.</p>
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<p>It is a very good way to set dropout outside the layers. Since not all
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PaddlePaddle layer support dropout, you can add an add_to layer, set
dropout here.
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Please refer to dropout_layer for details.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>LayerOutput|list|tuple</em>) &#8211; Input layers. It could be a LayerOutput or list/tuple of
LayerOutput.</li>
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<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type, default is tanh.</li>
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<li><strong>bias_attr</strong> (<em>ParameterAttribute|bool</em>) &#8211; Bias attribute. If False, means no bias. None is default
bias.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
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<div class="section" id="linear-comb-layer">
<h2>linear_comb_layer<a class="headerlink" href="#linear-comb-layer" title="Permalink to this headline"></a></h2>
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<dl class="function">
<dt>
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<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">linear_comb_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><dl class="docutils">
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<dt>A layer for weighted sum of vectors takes two inputs.</dt>
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<dd><ul class="first last">
<li><dl class="first docutils">
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<dt>Input: size of weights is M</dt>
<dd><p class="first last">size of vectors is M*N</p>
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</dd>
</dl>
</li>
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<li><p class="first">Output: a vector of size=N</p>
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</li>
</ul>
</dd>
</dl>
<div class="math">
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\[z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)\]</div>
<p>where <span class="math">\(0 \le i \le N-1\)</span></p>
<p>Or in the matrix notation:</p>
<div class="math">
\[z = x^\mathrm{T} Y\]</div>
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<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
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<li><span class="math">\(x\)</span>: weights</li>
<li><span class="math">\(y\)</span>: vectors.</li>
<li><span class="math">\(z\)</span>: the output.</li>
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</ul>
</dd>
</dl>
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<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
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<p>The simple usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">linear_comb</span> <span class="o">=</span> <span class="n">linear_comb_layer</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">vectors</span><span class="o">=</span><span class="n">vectors</span><span class="p">,</span>
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                                <span class="n">size</span><span class="o">=</span><span class="n">elem_dim</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>weights</strong> (<em>LayerOutput</em>) &#8211; The weight layer.</li>
<li><strong>vectors</strong> (<em>LayerOutput</em>) &#8211; The vector layer.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; the dimension of this layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="interpolation-layer">
<h2>interpolation_layer<a class="headerlink" href="#interpolation-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">interpolation_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This layer is for linear interpolation with two inputs,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]\]</div>
<p>where <span class="math">\(x_1\)</span> and <span class="math">\(x_2\)</span> are two (batchSize x dataDim) inputs,
<span class="math">\(w\)</span> is (batchSize x 1) weight vector, and <span class="math">\(y\)</span> is
(batchSize x dataDim) output.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">interpolation</span> <span class="o">=</span> <span class="n">interpolation_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer3</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>list|tuple</em>) &#8211; Input layer.</li>
<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="power-layer">
<h2>power_layer<a class="headerlink" href="#power-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">power_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This layer applies a power function to a vector element-wise,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[y = x^w\]</div>
<p>where <span class="math">\(x\)</span> is a input vector, <span class="math">\(w\)</span> is scalar weight,
and <span class="math">\(y\)</span> is a output vector.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">power</span> <span class="o">=</span> <span class="n">power_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="scaling-layer">
<h2>scaling_layer<a class="headerlink" href="#scaling-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">scaling_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>A layer for multiplying input vector by weight scalar.</p>
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<div class="math">
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\[y  = w x\]</div>
<p>where <span class="math">\(x\)</span> is size=dataDim input, <span class="math">\(w\)</span> is size=1 weight,
and <span class="math">\(y\)</span> is size=dataDim output.</p>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
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<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">scale</span> <span class="o">=</span> <span class="n">scaling_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="slope-intercept-layer">
<h2>slope_intercept_layer<a class="headerlink" href="#slope-intercept-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">slope_intercept_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This layer for applying a slope and an intercept to the input
element-wise. There is no activation and weight.</p>
<div class="math">
\[y = slope * x + intercept\]</div>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">scale</span> <span class="o">=</span> <span class="n">slope_intercept_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">slope</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>slope</strong> (<em>float.</em>) &#8211; the scale factor.</li>
<li><strong>intercept</strong> (<em>float.</em>) &#8211; the offset.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="tensor-layer">
<h2>tensor_layer<a class="headerlink" href="#tensor-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">tensor_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>This layer performs tensor operation for two input.
For example, each sample:</p>
<div class="math">
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\[y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1\]</div>
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<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
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<li><span class="math">\(a\)</span>: the first input contains M elements.</li>
<li><span class="math">\(b\)</span>: the second input contains N elements.</li>
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<li><span class="math">\(y_{i}\)</span>: the i-th element of y.</li>
<li><span class="math">\(W_{i}\)</span>: the i-th learned weight, shape if [M, N]</li>
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<li><span class="math">\(b^\mathrm{T}\)</span>: the transpose of <span class="math">\(b_{2}\)</span>.</li>
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</ul>
</dd>
</dl>
<p>The simple usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor_layer</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
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</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name</li>
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<li><strong>a</strong> (<em>LayerOutput</em>) &#8211; Input layer a.</li>
<li><strong>b</strong> (<em>LayerOutput</em>) &#8211; input layer b.</li>
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<li><strong>size</strong> (<em>int.</em>) &#8211; the layer dimension.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type. Default is tanh.</li>
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<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; The Parameter Attribute.</li>
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<li><strong>bias_attr</strong> (<em>ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute|None</em>) &#8211; Extra Layer config.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
Y
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</td>
</tr>
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<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="cos-sim">
<h2>cos_sim<a class="headerlink" href="#cos-sim" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">cos_sim</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Cosine Similarity Layer. The cosine similarity equation is here.</p>
<div class="math">
\[similarity = cos(\theta) = {\mathbf{a} \cdot \mathbf{b}
\over \|\mathbf{a}\| \|\mathbf{b}\|}\]</div>
<p>The size of a is M, size of b is M*N,
Similarity will be calculated N times by step M. The output size is
N. The scale will be multiplied to similarity.</p>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name</li>
<li><strong>a</strong> (<em>LayerOutput</em>) &#8211; input layer a</li>
<li><strong>b</strong> (<em>LayerOutput</em>) &#8211; input layer b</li>
<li><strong>scale</strong> (<em>float</em>) &#8211; scale for cosine value. default is 5.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; layer size. NOTE size_a * size should equal size_b.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="trans-layer">
<h2>trans_layer<a class="headerlink" href="#trans-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">trans_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for transposition.</p>
<div class="math">
\[y = x^\mathrm{T}\]</div>
<p>where <span class="math">\(x\)</span> is (M x N) input, and <span class="math">\(y\)</span> is (N x M) output.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">trans</span> <span class="o">=</span> <span class="n">trans_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="sampling-layers">
<h1>Sampling Layers<a class="headerlink" href="#sampling-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="maxid-layer">
<h2>maxid_layer<a class="headerlink" href="#maxid-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">maxid_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for finding the id which has the maximal value for each sample.
The result is stored in output.ids.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">maxid</span> <span class="o">=</span> <span class="n">maxid_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer name.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="sampling-id-layer">
<h2>sampling_id_layer<a class="headerlink" href="#sampling-id-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">sampling_id_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for sampling id from multinomial distribution from the input layer.
Sampling one id for one sample.</p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">samping_id</span> <span class="o">=</span> <span class="n">sampling_id_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="cost-layers">
<h1>Cost Layers<a class="headerlink" href="#cost-layers" title="Permalink to this headline"></a></h1>
<div class="section" id="cross-entropy">
<h2>cross_entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">cross_entropy</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A loss layer for multi class entropy.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>type</strong> (<em>basestring.</em>) &#8211; The type of cost.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
</ul>
</td>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="cross-entropy-with-selfnorm">
<h2>cross_entropy_with_selfnorm<a class="headerlink" href="#cross-entropy-with-selfnorm" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">cross_entropy_with_selfnorm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A loss layer for multi class entropy with selfnorm.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">cross_entropy_with_selfnorm</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>type</strong> (<em>basestring.</em>) &#8211; The type of cost.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>softmax_selfnorm_alpha</strong> (<em>float.</em>) &#8211; The scale factor affects the cost.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="multi-binary-label-cross-entropy">
<h2>multi_binary_label_cross_entropy<a class="headerlink" href="#multi-binary-label-cross-entropy" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">multi_binary_label_cross_entropy</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A loss layer for multi binary label cross entropy.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">multi_binary_label_cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>type</strong> (<em>basestring</em>) &#8211; The type of cost.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="huber-cost">
<h2>huber_cost<a class="headerlink" href="#huber-cost" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">huber_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A loss layer for huber loss.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_cost</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput.</em>) &#8211; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="lambda-cost">
<h2>lambda_cost<a class="headerlink" href="#lambda-cost" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">lambda_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>lambdaCost for lambdaRank LTR approach.</p>
<p>The simple usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">lambda_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                   <span class="n">score</span><span class="o">=</span><span class="n">score</span><span class="p">,</span>
                   <span class="n">NDCG_num</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
                   <span class="n">max_sort_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Samples of the same query should be loaded as sequence.</li>
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<li><strong>score</strong> &#8211; The 2nd input. Score of each sample.</li>
<li><strong>NDCG_num</strong> (<em>int</em>) &#8211; The size of NDCG (Normalized Discounted Cumulative Gain),
e.g., 5 for NDCG&#64;5. It must be less than for equal to the
minimum size of lists.</li>
<li><strong>max_sort_size</strong> (<em>int</em>) &#8211; The size of partial sorting in calculating gradient.
If max_sort_size = -1, then for each list, the
algorithm will sort the entire list to get gradient.
In other cases, max_sort_size must be greater than or
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equal to NDCG_num. And if max_sort_size is greater
than the size of a list, the algorithm will sort the
entire list of get gradient.</li>
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<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="rank-cost">
<h2>rank_cost<a class="headerlink" href="#rank-cost" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">rank_cost</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
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<dd><p>A cost Layer for learning to rank using gradient descent. Details can refer
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to <a class="reference external" href="http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf">papers</a>.
This layer contains at least three inputs. The weight is an optional
argument, which affects the cost.</p>
<div class="math">
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\[ \begin{align}\begin{aligned}C_{i,j} &amp; = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})\\o_{i,j} &amp; =  o_i - o_j\\\tilde{P_{i,j}} &amp; = \{0, 0.5, 1\} \ or \ \{0, 1\}\end{aligned}\end{align} \]</div>
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<dl class="docutils">
<dt>In this formula:</dt>
<dd><ul class="first last simple">
<li><span class="math">\(C_{i,j}\)</span> is the cross entropy cost.</li>
<li><span class="math">\(\tilde{P_{i,j}}\)</span> is the label. 1 means positive order
and 0 means reverse order.</li>
<li><span class="math">\(o_i\)</span> and <span class="math">\(o_j\)</span>: the left output and right output.
Their dimension is one.</li>
</ul>
</dd>
</dl>
<p>The simple usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">rank_cost</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="n">out_left</span><span class="p">,</span>
                 <span class="n">right</span><span class="o">=</span><span class="n">out_right</span><span class="p">,</span>
                 <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>left</strong> (<em>LayerOutput</em>) &#8211; The first input, the size of this layer is 1.</li>
<li><strong>right</strong> (<em>LayerOutput</em>) &#8211; The right input, the size of this layer is 1.</li>
<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label is 1 or 0, means positive order and reverse order.</li>
<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; The weight affects the cost, namely the scale of cost.
It is an optional argument.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="crf-layer">
<h2>crf_layer<a class="headerlink" href="#crf-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">crf_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for calculating the cost of sequential conditional random
field model.</p>
<p>The simple usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">crf</span> <span class="o">=</span> <span class="n">crf_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
                <span class="n">size</span><span class="o">=</span><span class="n">label_dim</span><span class="p">)</span>
</pre></div>
</div>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The first input layer is the feature.</li>
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<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; The second input layer is label.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; The category number.</li>
<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; The third layer is &#8220;weight&#8221; of each sample, which is an
optional argument.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter attribute. None means default attribute</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="crf-decoding-layer">
<h2>crf_decoding_layer<a class="headerlink" href="#crf-decoding-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">crf_decoding_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for calculating the decoding sequence of sequential conditional
random field model. The decoding sequence is stored in output.ids.
If a second input is provided, it is treated as the ground-truth label, and
this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The first input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; size of this layer.</li>
<li><strong>label</strong> (<em>LayerOutput or None</em>) &#8211; None or ground-truth label.</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter attribute. None means default attribute</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="ctc-layer">
<h2>ctc_layer<a class="headerlink" href="#ctc-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">ctc_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Connectionist Temporal Classification (CTC) is designed for temporal
classication task. That is, for sequence labeling problems where the
alignment between the inputs and the target labels is unknown.</p>
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<p>More details can be found by referring to <a class="reference external" href="http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf">Connectionist Temporal
Classification: Labelling Unsegmented Sequence Data with Recurrent
Neural Networks</a></p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Considering the &#8216;blank&#8217; label needed by CTC, you need to use
(num_classes + 1) as the input size. num_classes is the category number.
And the &#8216;blank&#8217; is the last category index. So the size of &#8216;input&#8217; layer, such as
fc_layer with softmax activation, should be num_classes + 1. The size of ctc_layer
should also be num_classes + 1.</p>
</div>
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<p>The simple usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ctc</span> <span class="o">=</span> <span class="n">ctc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
                <span class="n">size</span><span class="o">=</span><span class="mi">9055</span><span class="p">,</span>
                <span class="n">norm_by_times</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</pre></div>
</div>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layer.</li>
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<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; The data layer of label with variable length.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; category numbers + 1.</li>
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<li><strong>name</strong> (<em>basestring|None</em>) &#8211; The name of this layer</li>
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<li><strong>norm_by_times</strong> (<em>bool</em>) &#8211; Whether to normalization by times. False by default.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="hsigmoid">
<h2>hsigmoid<a class="headerlink" href="#hsigmoid" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">hsigmoid</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Organize the classes into a binary tree. At each node, a sigmoid function
is used to calculate the probability of belonging to the right branch.
This idea is from &#8220;F. Morin, Y. Bengio (AISTATS 05):
Hierarchical Probabilistic Neural Network Language Model.&#8221;</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">hsigmoid</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span>
                <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">,</span>
                <span class="n">num_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput|list|tuple</em>) &#8211; Input layers. It could be a LayerOutput or list/tuple of
LayerOutput.</li>
<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label layer.</li>
<li><strong>num_classes</strong> (<em>int</em>) &#8211; number of classes.</li>
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<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name</li>
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<li><strong>bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Bias attribute. None means default bias.
False means no bias.</li>
<li><strong>layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="check-layer">
<h1>Check Layer<a class="headerlink" href="#check-layer" title="Permalink to this headline"></a></h1>
<div class="section" id="eos-layer">
<h2>eos_layer<a class="headerlink" href="#eos-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.layers.</code><code class="descname">eos_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A layer for checking EOS for each sample:
- output_id = (input_id == conf.eos_id)</p>
<p>The result is stored in output_.ids.
It is used by recurrent layer group.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">eos</span> <span class="o">=</span> <span class="n">eos_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">eos_id</span><span class="o">=</span><span class="nb">id</span><span class="p">)</span>
</pre></div>
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<col class="field-name" />
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
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<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer name.</li>
<li><strong>eos_id</strong> (<em>int</em>) &#8211; end id of sequence</li>
<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute.</em>) &#8211; extra layer attributes.</li>
</ul>
</td>
</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object.</p>
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</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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</div>


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  <h3><a href="../../../index.html">Table Of Contents</a></h3>
  <ul>
<li><a class="reference internal" href="#">Base</a><ul>
<li><a class="reference internal" href="#layertype">LayerType</a></li>
<li><a class="reference internal" href="#layeroutput">LayerOutput</a></li>
</ul>
</li>
<li><a class="reference internal" href="#data-layer">Data layer</a><ul>
<li><a class="reference internal" href="#id1">data_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#fully-connected-layers">Fully Connected Layers</a><ul>
<li><a class="reference internal" href="#fc-layer">fc_layer</a></li>
<li><a class="reference internal" href="#selective-fc-layer">selective_fc_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#conv-layers">Conv Layers</a><ul>
<li><a class="reference internal" href="#conv-operator">conv_operator</a></li>
<li><a class="reference internal" href="#conv-shift-layer">conv_shift_layer</a></li>
<li><a class="reference internal" href="#img-conv-layer">img_conv_layer</a></li>
<li><a class="reference internal" href="#context-projection">context_projection</a></li>
</ul>
</li>
<li><a class="reference internal" href="#image-pooling-layer">Image Pooling Layer</a><ul>
<li><a class="reference internal" href="#img-pool-layer">img_pool_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#norm-layer">Norm Layer</a><ul>
<li><a class="reference internal" href="#img-cmrnorm-layer">img_cmrnorm_layer</a></li>
<li><a class="reference internal" href="#batch-norm-layer">batch_norm_layer</a></li>
<li><a class="reference internal" href="#sum-to-one-norm-layer">sum_to_one_norm_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#recurrent-layers">Recurrent Layers</a><ul>
<li><a class="reference internal" href="#recurrent-layer">recurrent_layer</a></li>
<li><a class="reference internal" href="#lstmemory">lstmemory</a></li>
<li><a class="reference internal" href="#lstm-step-layer">lstm_step_layer</a></li>
<li><a class="reference internal" href="#grumemory">grumemory</a></li>
<li><a class="reference internal" href="#gru-step-layer">gru_step_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#recurrent-layer-group">Recurrent Layer Group</a><ul>
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<li><a class="reference internal" href="#recurrent-group">recurrent_group</a></li>
<li><a class="reference internal" href="#beam-search">beam_search</a></li>
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<li><a class="reference internal" href="#get-output-layer">get_output_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#mixed-layer">Mixed Layer</a><ul>
<li><a class="reference internal" href="#id2">mixed_layer</a></li>
<li><a class="reference internal" href="#embedding-layer">embedding_layer</a></li>
<li><a class="reference internal" href="#dotmul-projection">dotmul_projection</a></li>
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<li><a class="reference internal" href="#dotmul-operator">dotmul_operator</a></li>
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<li><a class="reference internal" href="#full-matrix-projection">full_matrix_projection</a></li>
<li><a class="reference internal" href="#identity-projection">identity_projection</a></li>
<li><a class="reference internal" href="#table-projection">table_projection</a></li>
<li><a class="reference internal" href="#trans-full-matrix-projection">trans_full_matrix_projection</a></li>
</ul>
</li>
<li><a class="reference internal" href="#aggregate-layers">Aggregate Layers</a><ul>
<li><a class="reference internal" href="#pooling-layer">pooling_layer</a></li>
<li><a class="reference internal" href="#last-seq">last_seq</a></li>
<li><a class="reference internal" href="#first-seq">first_seq</a></li>
<li><a class="reference internal" href="#concat-layer">concat_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#reshaping-layers">Reshaping Layers</a><ul>
<li><a class="reference internal" href="#block-expand-layer">block_expand_layer</a></li>
<li><a class="reference internal" href="#expand-layer">expand_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#math-layers">Math Layers</a><ul>
<li><a class="reference internal" href="#addto-layer">addto_layer</a></li>
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<li><a class="reference internal" href="#linear-comb-layer">linear_comb_layer</a></li>
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<li><a class="reference internal" href="#interpolation-layer">interpolation_layer</a></li>
<li><a class="reference internal" href="#power-layer">power_layer</a></li>
<li><a class="reference internal" href="#scaling-layer">scaling_layer</a></li>
<li><a class="reference internal" href="#slope-intercept-layer">slope_intercept_layer</a></li>
<li><a class="reference internal" href="#tensor-layer">tensor_layer</a></li>
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<li><a class="reference internal" href="#cos-sim">cos_sim</a></li>
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<li><a class="reference internal" href="#trans-layer">trans_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#sampling-layers">Sampling Layers</a><ul>
<li><a class="reference internal" href="#maxid-layer">maxid_layer</a></li>
<li><a class="reference internal" href="#sampling-id-layer">sampling_id_layer</a></li>
</ul>
</li>
<li><a class="reference internal" href="#cost-layers">Cost Layers</a><ul>
<li><a class="reference internal" href="#cross-entropy">cross_entropy</a></li>
<li><a class="reference internal" href="#cross-entropy-with-selfnorm">cross_entropy_with_selfnorm</a></li>
<li><a class="reference internal" href="#multi-binary-label-cross-entropy">multi_binary_label_cross_entropy</a></li>
<li><a class="reference internal" href="#huber-cost">huber_cost</a></li>
<li><a class="reference internal" href="#lambda-cost">lambda_cost</a></li>
<li><a class="reference internal" href="#rank-cost">rank_cost</a></li>
<li><a class="reference internal" href="#crf-layer">crf_layer</a></li>
<li><a class="reference internal" href="#crf-decoding-layer">crf_decoding_layer</a></li>
<li><a class="reference internal" href="#ctc-layer">ctc_layer</a></li>
<li><a class="reference internal" href="#hsigmoid">hsigmoid</a></li>
</ul>
</li>
<li><a class="reference internal" href="#check-layer">Check Layer</a><ul>
<li><a class="reference internal" href="#eos-layer">eos_layer</a></li>
</ul>
</li>
</ul>

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